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Data Platform Architect
Chennai, India - Remote
Live, first seen by us 77 days ago- Pay
- Pay not listed
- Work setting
Remote — work setting source
Listed location: Chennai, India - Remote
Read the full posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingWe are seeking an Enterprise Data Platform Architect to design and build a next-generation, vendor-agnostic Data Platform from the ground up.
What You Will Own: • Custom Platform Architecture: Lead design decisions for a decoupled Lakehouse architecture on AWS and Kubernetes, establishing storage layout, partition evolution, and open table formats (Apache Iceberg).
Lead decoupled Lakehouse architecture on AWS and Kubernetes using Apache Iceberg
Match workloads to engines such as DuckDB, PyIceberg, and serverless query engines
Build config-driven containerized pipeline runners with idempotency and schema evolution
Architect governance with column-level security, PII discovery, and audit trails
Design multi-layer data quality gates that halt pipelines before bad data spreads
From the employer’s posting
We are seeking an Enterprise Data Platform Architect to design and build a next-generation, vendor-agnostic Data Platform from the ground up. You will own the foundational platform architecture—decoupling compute from storage using Apache Iceberg, designing containerized execution engines on Kubernetes, establishing multi-engine processing (DuckDB, PyIceberg, dbt, serverless query engines), and embedding automated governance from Day 1.
What You Will Own: • Custom Platform Architecture: Lead design decisions for a decoupled Lakehouse architecture on AWS and Kubernetes, establishing storage layout, partition evolution, and open table formats (Apache Iceberg). • Multi-Engine Compute Optimization: Match specific workloads to appropriate engines (e.g., DuckDB for in-memory batch processing, PyIceberg for atomic writes, serverless engines for query serving) to maximize speed while minimizing cloud spend. • Framework & Runner Engineering: Define config-driven pipeline patterns and containerized execution runners that automatically handle idempotency, schema evolution, advisory locking, and step-level logging. • Embedded Governance & Cataloging: Architect automated, zero-trust data governance from Day 1, integrating open-source catalogs (Apache Polaris, Glue) with column-level security, PII discovery, and automated audit trails. • Data Quality Frameworks: Design multi-layer, config-driven data quality gates that execute inline in-memory and trigger automated pipeline halts before bad data propagates. • Architectural Decision Records (ADRs): Maintain a version-controlled repository of technical decisions, documenting trade-offs, compute boundaries, and long-term implications.
Lead design decisions for a decoupled Lakehouse architecture on AWS and Kubernetes
Match specific workloads to appropriate engines (e.g., DuckDB for in-memory batch processing, PyIceberg for atomic writes, serverless engines for query serving)
Define config-driven pipeline patterns and containerized execution runners that automatically handle idempotency, schema evolution, advisory locking, and step-level logging
Architect automated, zero-trust data governance from Day 1, integrating open-source catalogs (Apache Polaris, Glue) with column-level security, PII discovery, and automated audit trails
Design multi-layer, config-driven data quality gates that execute inline in-memory and trigger automated pipeline halts before bad data propagates
Posting record
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- Live
- First seen by us
- Aug 18, 202677 days ago
- Recorded sightings
- 226
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
TaskUs is hiring
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Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- SQL
- AWS
- Databricks
- dbt
- Delta Lake
- DuckDB
- Iceberg
- Kubernetes
- S3
- Snowflake
- Airflow
Where the posting names them
What You Will Own: • Custom Platform Architecture: Lead design decisions for a decoupled Lakehouse architecture on AWS and Kubernetes, establishing storage layout, partition evolution, and open table formats (Apache Iceberg). • Multi-Engine Compute Optimization: Match specific workloads to appropriate engines (e.g., DuckDB for in-memory batch processing, PyIceberg for atomic writes, serverless engines for query serving) to maximize speed while minimizing cloud spend. • Framework & Runner Engineering: Define config-driven pipeline patterns and containerized execution runners that automatically handle idempotency, schema evolution, advisory locking, and step-level logging. • Embedded Governance & Cataloging: Architect automated, zero-trust data governance from Day 1, integrating open-source catalogs (Apache Polaris, Glue) with column-level security, PII discovery, and automated audit trails. • Data Quality Frameworks: Design multi-layer, config-driven data quality gates that execute inline in-memory and trigger automated pipeline halts before bad data propagates. • Architectural Decision Records (ADRs): Maintain a version-controlled repository of technical decisions, documenting trade-offs, compute boundaries, and long-term implications. Core Technical Qualifications: • 8+ Years in Platform Architecture / Infrastructure Engineering: Proven track record of designing and deploying production-grade, open-standard Data Platforms from scratch. • Open Table Formats & Decoupled Storage: Deep expertise in Apache Iceberg (or Delta Lake), including native PyIceberg APIs, ACID transactions on object storage (S3), schema/partition evolution, and metadata management. • High-Efficiency & Embedded Compute: Hands-on experience with in-process/vectorized engines (DuckDB, PyArrow) and matching workload scales to the right compute engine (single-node vs. distributed). • Kubernetes & Container Orchestration: Experience in Kubernetes (EKS/K8s)—specifically Pods, Deployments, ConfigMaps, Secrets, and custom containerized execution runners. • Advanced Orchestration: Expert-level Apache Airflow skills, specifically designing dynamic, dependency-aware DAGs and managing worker architecture in resource-constrained environments. • Transformation & Semantic Layers: Deep proficiency with dbt Core (SQL compilation/modeling) paired with external execution engines and semantic YAML layer definitions. • Open-Source Catalogs & Governance: Experience implementing open catalog specifications (Apache Polaris, Iceberg REST Catalog) and metadata platforms (OpenMetadata, AWS Lake Formation). • Cloud Infrastructure (AWS): Advanced knowledge of AWS core services (S3, EKS, IAM, EC2, Serverless Query Serving) with strict adherence to storage-compute separation and FinOps spend optimization.
Work Mode : Remote What You Will Own: • Custom Platform Architecture: Lead design decisions for a decoupled Lakehouse architecture on AWS and Kubernetes, establishing storage layout, partition evolution, and open table formats (Apache Iceberg). • Multi-Engine Compute Optimization: Match specific workloads to appropriate engines (e.g., DuckDB for in-memory batch processing, PyIceberg for atomic writes, serverless engines for query serving) to maximize speed while minimizing cloud spend. • Framework & Runner Engineering: Define config-driven pipeline patterns and containerized execution runners that automatically handle idempotency, schema evolution, advisory locking, and step-level logging. • Embedded Governance & Cataloging: Architect automated, zero-trust data governance from Day 1, integrating open-source catalogs (Apache Polaris, Glue) with column-level security, PII discovery, and automated audit trails. • Data Quality Frameworks: Design multi-layer, config-driven data quality gates that execute inline in-memory and trigger automated pipeline halts before bad data propagates. • Architectural Decision Records (ADRs): Maintain a version-controlled repository of technical decisions, documenting trade-offs, compute boundaries, and long-term implications. Core Technical Qualifications: • 8+ Years in Platform Architecture / Infrastructure Engineering: Proven track record of designing and deploying production-grade, open-standard Data Platforms from scratch. • Open Table Formats & Decoupled Storage: Deep expertise in Apache Iceberg (or Delta Lake), including native PyIceberg APIs, ACID transactions on object storage (S3), schema/partition evolution, and metadata management. • High-Efficiency & Embedded Compute: Hands-on experience with in-process/vectorized engines (DuckDB, PyArrow) and matching workload scales to the right compute engine (single-node vs. distributed). • Kubernetes & Container Orchestration: Experience in Kubernetes (EKS/K8s)—specifically Pods, Deployments, ConfigMaps, Secrets, and custom containerized execution runners. • Advanced Orchestration: Expert-level Apache Airflow skills, specifically designing dynamic, dependency-aware DAGs and managing worker architecture in resource-constrained environments. • Transformation & Semantic Layers: Deep proficiency with dbt Core (SQL compilation/modeling) paired with external execution engines and semantic YAML layer definitions. • Open-Source Catalogs & Governance: Experience implementing open catalog specifications (Apache Polaris, Iceberg REST Catalog) and metadata platforms
Core Technical Qualifications: • 8+ Years in Platform Architecture / Infrastructure Engineering: Proven track record of designing and deploying production-grade, open-standard Data Platforms from scratch. • Open Table Formats & Decoupled Storage: Deep expertise in Apache Iceberg (or Delta Lake), including native PyIceberg APIs, ACID transactions on object storage (S3), schema/partition evolution, and metadata management. • High-Efficiency & Embedded Compute: Hands-on experience with in-process/vectorized engines (DuckDB, PyArrow) and matching workload scales to the right compute engine (single-node vs. distributed). • Kubernetes & Container Orchestration: Experience in Kubernetes (EKS/K8s)—specifically Pods, Deployments, ConfigMaps, Secrets, and custom containerized execution runners. • Advanced Orchestration: Expert-level Apache Airflow skills, specifically designing dynamic, dependency-aware DAGs and managing worker architecture in resource-constrained environments. • Transformation & Semantic Layers: Deep proficiency with dbt Core (SQL compilation/modeling) paired with external execution engines and semantic YAML layer definitions. • Open-Source Catalogs & Governance: Experience implementing open catalog specifications (Apache Polaris, Iceberg REST Catalog) and metadata platforms (OpenMetadata, AWS Lake Formation). • Cloud Infrastructure (AWS): Advanced knowledge of AWS core services (S3, EKS, IAM, EC2, Serverless Query Serving) with strict adherence to storage-compute separation and FinOps spend optimization. Preferred / Bonus Qualifications (Pluses): • Databricks & Snowflake Experience (Plus): Experience with Databricks (Lakehouse/Unity Catalog) or Snowflake is a plus, particularly in the context of legacy migrations, hybrid query serving, or cross-engine query federation. • DataOps & CI/CD: Hands-on experience establishing automated testing, blue/green deployment patterns for data models, and GitOps workflows. • Executive Communication: Proven track record translating technical debt, compute benchmarks, and architectural roadmaps for executive leadership and non-technical stakeholders.
(OpenMetadata, AWS Lake Formation). • Cloud Infrastructure (AWS): Advanced knowledge of AWS core services (S3, EKS, IAM, EC2, Serverless Query Serving) with strict adherence to storage-compute separation and FinOps spend optimization. Preferred / Bonus Qualifications (Pluses): • Databricks & Snowflake Experience (Plus): Experience with Databricks (Lakehouse/Unity Catalog) or Snowflake is a plus, particularly in the context of legacy migrations, hybrid query serving, or cross-engine query federation. • DataOps & CI/CD: Hands-on experience establishing automated testing, blue/green deployment patterns for data models, and GitOps workflows. • Executive Communication: Proven track record translating technical debt, compute benchmarks, and architectural roadmaps for executive leadership and non-technical stakeholders. How We Partner To Protect You: TaskUs will neither solicit money from you during your application process nor require any form of payment in order to proceed with your application. Kindly ensure that you are always in communication with only authorized recruiters of TaskUs.
Role Overview: We are seeking an Enterprise Data Platform Architect to design and build a next-generation, vendor-agnostic Data Platform from the ground up. You will own the foundational platform architecture—decoupling compute from storage using Apache Iceberg, designing containerized execution engines on Kubernetes, establishing multi-engine processing (DuckDB, PyIceberg, dbt, serverless query engines), and embedding automated governance from Day 1. Work Mode : Remote
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About the role
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
US government figures for computer and information systems managers
- Median yearly pay, May 2025
- $175,140
- Expected change, 2025 to 2035
- 15.8% more jobsagainst 3.5% for all jobs
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Job description
About TaskUs: TaskUs is a provider of outsourced digital services and next-generation customer experience to fast-growing technology companies, helping its clients represent, protect and grow their brands. Leveraging a cloud-based infrastructure, TaskUs serves clients in the fastest-growing sectors, including social media, e-commerce, gaming, streaming media, food delivery, ride-sharing, HiTech, FinTech, and HealthTech.
The People First culture at TaskUs has enabled the company to expand its workforce to approximately 60,000 employees globally. Presently, we have a presence in twenty-three locations across twelve countries, which include the Philippines, India, and the United States.
It started with one ridiculously good idea to create a different breed of Business Processing Outsourcing (BPO)! We at TaskUs understand that achieving growth for our partners requires a culture of constant motion, exploring new technologies, being ready to handle any challenge at a moment’s notice, and mastering consistency in an ever-changing world.
What We Offer: At TaskUs, we prioritize our employees' well-being by offering competitive industry salaries and comprehensive benefits packages. Our commitment to a People First culture is reflected in the various departments we have established, including Total Rewards, Wellness, HR, and Diversity. We take pride in our inclusive environment and positive impact on the community. Moreover, we actively encourage internal mobility and professional growth at all stages of an employee's career within TaskUs. Join our team today and experience firsthand our dedication to supporting People First.
Role Overview:
We are seeking an Enterprise Data Platform Architect to design and build a
next-generation, vendor-agnostic Data Platform from the ground up. You will own the
foundational platform architecture—decoupling compute from storage using Apache Iceberg,
designing containerized execution engines on Kubernetes, establishing multi-engine processing
(DuckDB, PyIceberg, dbt, serverless query engines), and embedding automated governance
from Day 1.
Work Mode : Remote
What You Will Own:
• Custom Platform Architecture: Lead design decisions for a decoupled Lakehouse
architecture on AWS and Kubernetes, establishing storage layout, partition evolution, and
open table formats (Apache Iceberg).
• Multi-Engine Compute Optimization: Match specific workloads to appropriate engines (e.g.,
DuckDB for in-memory batch processing, PyIceberg for atomic writes, serverless engines
for query serving) to maximize speed while minimizing cloud spend.
• Framework & Runner Engineering: Define config-driven pipeline patterns and containerized
execution runners that automatically handle idempotency, schema evolution, advisory
locking, and step-level logging.
• Embedded Governance & Cataloging: Architect automated, zero-trust data governance
from Day 1, integrating open-source catalogs (Apache Polaris, Glue) with column-level
security, PII discovery, and automated audit trails.
• Data Quality Frameworks: Design multi-layer, config-driven data quality gates that execute
inline in-memory and trigger automated pipeline halts before bad data propagates.
• Architectural Decision Records (ADRs): Maintain a version-controlled repository of
technical decisions, documenting trade-offs, compute boundaries, and long-term
implications.
Core Technical Qualifications:
• 8+ Years in Platform Architecture / Infrastructure Engineering: Proven track record of
designing and deploying production-grade, open-standard Data Platforms from scratch.
• Open Table Formats & Decoupled Storage: Deep expertise in Apache Iceberg (or Delta
Lake), including native PyIceberg APIs, ACID transactions on object storage (S3),
schema/partition evolution, and metadata management.
• High-Efficiency & Embedded Compute: Hands-on experience with in-process/vectorized
engines (DuckDB, PyArrow) and matching workload scales to the right compute engine
(single-node vs. distributed).
• Kubernetes & Container Orchestration: Experience in Kubernetes (EKS/K8s)—specifically
Pods, Deployments, ConfigMaps, Secrets, and custom containerized execution runners.
• Advanced Orchestration: Expert-level Apache Airflow skills, specifically designing dynamic,
dependency-aware DAGs and managing worker architecture in resource-constrained
environments.
• Transformation & Semantic Layers: Deep proficiency with dbt Core (SQL
compilation/modeling) paired with external execution engines and semantic YAML layer
definitions.
• Open-Source Catalogs & Governance: Experience implementing open catalog
specifications (Apache Polaris, Iceberg REST Catalog) and metadata platforms
(OpenMetadata, AWS Lake Formation).
• Cloud Infrastructure (AWS): Advanced knowledge of AWS core services (S3, EKS, IAM,
EC2, Serverless Query Serving) with strict adherence to storage-compute separation and
FinOps spend optimization.
Preferred / Bonus Qualifications (Pluses):
• Databricks & Snowflake Experience (Plus): Experience with Databricks (Lakehouse/Unity
Catalog) or Snowflake is a plus, particularly in the context of legacy migrations, hybrid
query serving, or cross-engine query federation.
• DataOps & CI/CD: Hands-on experience establishing automated testing, blue/green
deployment patterns for data models, and GitOps workflows.
• Executive Communication: Proven track record translating technical debt, compute
benchmarks, and architectural roadmaps for executive leadership and non-technical
stakeholders.
How We Partner To Protect You: TaskUs will neither solicit money from you during your application process nor require any form of payment in order to proceed with your application. Kindly ensure that you are always in communication with only authorized recruiters of TaskUs.
DEI: In TaskUs we believe that innovation and higher performance are brought by people from all walks of life. We welcome applicants of different backgrounds, demographics, and circumstances. Inclusive and equitable practices are our responsibility as a business. TaskUs is committed to providing equal access to opportunities. If you need reasonable accommodations in any part of the hiring process, please let us know.
We invite you to explore all TaskUs career opportunities and apply through the provided URL https://www.taskus.com/careers/.
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- Location & working pattern
Chennai, India - Remote
(OpenMetadata, AWS Lake Formation). • Cloud Infrastructure (AWS): Advanced knowledge of AWS core services (S3, EKS, IAM, EC2, Serverless Query Serving) with strict adherence to storage-compute separation and FinOps spend optimization. Preferred / Bonus Qualifications (Pluses): • Databricks & Snowflake Experience (Plus): Experience with Databricks (Lakehouse/Unity Catalog) or Snowflake is a plus, particularly in the context of legacy migrations, hybrid query serving, or cross-engine query federation. • DataOps & CI/CD: Hands-on experience establishing automated testing, blue/green deployment patterns for data models, and GitOps workflows. • Executive Communication: Proven track record translating technical debt, compute benchmarks, and architectural roadmaps for executive leadership and non-technical stakeholders. How We Partner To Protect You: TaskUs will neither solicit money from you during your application process nor require any form of payment in order to proceed with your application. Kindly ensure that you are always in communication with only authorized recruiters of TaskUs.
More relevant text appears in the full description.
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